Titlebook: Computer Vision – ECCV 2012; 12th European Confer Andrew Fitzgibbon,Svetlana Lazebnik,Cordelia Schmi Conference proceedings 2012 Springer-V

EXULT

Fast Parameter Sensitivity Analysis of PDE-Based Image Processing Methods approach is not based on a sampling strategy, instead we combine the polynomial chaos expansion and stochastic finite elements with PDE-based image processing operators. With our approach and a moderate number of parameters in the models the full sensitivity analysis is obtained at the cost of a fe

一大群

The Lazy Flipper: Efficient Depth-Limited Exhaustive Search in Discrete Graphical Modelsle), it is guaranteed to converge to a global optimum, passing through a series of monotonously improving local optima that are guaranteed to be optimal within a given and increasing Hamming distance. For a search depth of 1, it specializes to ICM. Between these extremes, a tradeoff between approxim

步兵

FIN

Contraction Moves for Geometric Model Fittinges are particularly useful for optimizing the assignments in model fitting frameworks whose energies include . (LC), as well as . (MRF) terms. These problems benefit from the contraction moves’ greater scope for removing instances from the model, reducing label costs. We demonstrate this effect on t

发怨言

General and Nested Wiberg Minimization: ,, and Maximum Likelihoodratively minimizing ||. − .(.)||. with respect to . only. This approach factors the matrix while effectively removing . from the minimization. We generalize the Wiberg approach beyond factorization to minimize an arbitrary function that is nonlinear in each of two sets of variables. In this paper we

小卒

Nonmetric Priors for Continuous Multilabel Optimization(.,.) = 0 are required. In contrast to previous grid based approaches for the nonmetric case, the proposed prior is formulated in the continuous setting avoiding grid artifacts. In particular, the model is easy to implement, provides a convex relaxation for the Mumford-Shah functional and yields com

fetter

Real-Time Camera Tracking: When is High Frame-Rate Best?at the computation required would be too great. Actually, increasing frame-rate is mitigated by reduced computational cost . in trackers which take advantage of prediction. Additionally, when we consider the physics of image formation, high frame-rate implies that the upper bound on shutter time is

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Synapse

Continuous Regression for Non-rigid Image Alignmentsed for face alignment. Broadly speaking, PAMs methods can be classified into generative and discriminative. Discriminative methods learn a mapping between appearance features and motion parameters (rigid and non-rigid). While discriminative approaches have some advantages (e.g., feature weighting,

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